Are Your Prices Too Low?

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Your company sells sunglasses for $10. The unit cost is $7. You’re thinking about cutting the price by 50 cents. According to the best sales estimates, if you hold the price, you’ll have a 100% chance of selling 1,000 units. If you cut the price to $9.50, you’ll have an 80% chance of selling 1,250 units, and a 20% chance of selling only 1,000. What should you do?

Statistically speaking, options A and B are identical: Each produces a $3,000 profit. And since Option A is risk-free, it might seem like the logical choice. Yet when 60 managers responsible for pricing decisions were asked this question, most opted to reduce the price. When they were told that competitors were likely to match the cut, most still chose the cheaper price point. Even when they were informed that a new demand forecast showed that the cut would actually lead to lower profits, the majority still wanted to reduce the price.

This and many other studies indicate that pricing managers routinely set prices too low, sapping their companies’ profits. What would explain such counterproductive pricing decisions? I believe there are two psychological forces at work.

First is managers’ reliance on what marketing scholars call “hysteresis”—a type of market evolution pattern in which buying market share through aggressive short-term pricing leads to permanent increases in profitability. Although only one of several possible patterns of market evolution, hysteresis has a unique hold on the imagination thanks to certain highly memorable instances when price cuts did lead to enduring share gains. More often than not, however, price cuts are quickly matched by competitors. Any gain in market share is short-lived, and overall industry profits end up falling.

The second and more important force is managers’ natural tendency to want to make decisions that can be justified objectively. What could be wrong with that? The problem is that companies tend to lack hard data on the complex determinants of profitability, such as the relationship between price changes and sales volumes, the link between demand levels and costs, and the likely responses of competitors to price changes. In contrast, companies usually have rich, unambiguous information on costs, sales, market share, and competitors’ prices. As a result, managers tend to make pricing decisions based on current costs, projected short-term share gains, or current competitor prices rather than on long-term profitability.

Managers tend to make pricing decisions based on current costs, projected short-term share gains, or current competitor prices rather than on long-term profitability.

The problems can be tackled from two directions, quantitatively and psychologically. By linking information about price, cost, and demand within the same decision-support system, executives can get the hard data they need to calculate the effects of pricing decisions on profitability. Between 1995 and 1999, for instance, decision makers at Ford adopted a new pricing strategy by studying the demand at different price points as well as consumer perception of value-added features. In part by raising prices, Ford reported record earnings in 1999, even though it lost two points in market share. As Leonard Lodish, a professor at Wharton, has written, top executives need to create an environment that encourages pricing managers to make “vaguely right” decisions—in other words, decisions that take into account likely market responses, which can’t be encapsulated in concrete data. Pricing managers need to be encouraged to conduct pricing experiments and to speculate about competitors’ future moves and countermoves over the near and long terms.

Yes, it’s hard to estimate sales, costs, and profits at alternative price points. And it’s hard to predict competitors’ reactions well into the future. But if you ignore the complexities and ambiguities of pricing, you may end up leaving significant sums of money on the table.

A version of this article appeared in the October 2001 issue of Harvard Business Review.

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